tinyvit_to_mmpretrain.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from pathlib import Path

import torch


def convert_weights(weight):
    """Weight Converter.

    Converts the weights from timm to mmpretrain
    Args:
        weight (dict): weight dict from timm
    Returns:
        Converted weight dict for mmpretrain
    """
    result = dict()
    result['meta'] = dict()
    temp = dict()
    mapping = {
        'c.weight': 'conv2d.weight',
        'bn.weight': 'bn2d.weight',
        'bn.bias': 'bn2d.bias',
        'bn.running_mean': 'bn2d.running_mean',
        'bn.running_var': 'bn2d.running_var',
        'bn.num_batches_tracked': 'bn2d.num_batches_tracked',
        'layers': 'stages',
        'norm_head': 'norm3',
    }

    weight = weight['model']

    for k, v in weight.items():
        # keyword mapping
        for mk, mv in mapping.items():
            if mk in k:
                k = k.replace(mk, mv)

        if k.startswith('head.'):
            temp['head.fc.' + k[5:]] = v
        else:
            temp['backbone.' + k] = v

    result['state_dict'] = temp
    return result


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Convert model keys')
    parser.add_argument('src', help='src detectron model path')
    parser.add_argument('dst', help='save path')
    args = parser.parse_args()
    dst = Path(args.dst)
    if dst.suffix != '.pth':
        print('The path should contain the name of the pth format file.')
        exit(1)
    dst.parent.mkdir(parents=True, exist_ok=True)

    original_model = torch.load(args.src, map_location='cpu')
    converted_model = convert_weights(original_model)
    torch.save(converted_model, args.dst)